The recent computing performance revolution has driven improvements in sensor, communication, and storage technology. Multi-decadal remote sensing datasets at the petabyte scale are now available in commercial clouds, with new satellite constellations generating petabytes/year of daily high-resolution global coverage imagery. Cloud computing and storage, combined with recent advances in machine learning, are enabling understanding of the world at a scale and at a level of detail never before feasible. We show data processing at terabyte rates in the cloud using multi-modal sensor data and use the calibrated, georeferenced imagery to build videos of the Earth at varying temporal and spatial resolutions. Such temporal-spectral-spatial views of the world enable a range of climate monitoring and change-detection applications. Here we demonstrate one application by using MODIS satellite imagery temporal stacks to classify land cover over North America, and explore the use of synthetic aperture imagery (SAR) to enhance the resulting category mask.